Check computer science papers: citations, claims and retractions

SensoryGANs: An Effective Generative Adversarial Framework for Sensor-based Human Activity Recognition

Wang, J., Chen, Y., Gu, Y., Xiao, Y., & Pan, H. (2018) · 2018 International Joint Conference on Neural Networks (IJCNN)

Context-Aware Activity Recognition SystemsGait Recognition and Analysis

Cited by 92

Voice as a Design Material: Sociophonetic Inspired Design Strategies in Human-Computer Interaction

Sutton, S. J., Foulkes, P., Kirk, D., & Lawson, S. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Speech and dialogue systemsInnovative Human-Technology Interaction

Cited by 92

Re3: Generating Longer Stories With Recursive Reprompting and Revision

Yang, K., Tian, Y., Peng, N., & Klein, D. (2022) · arXiv

Artificial Intelligence in GamesTopic Modeling

Cited by 91

Sexist Slurs: Reinforcing Feminine Stereotypes Online

Felmlee, D., Inara Rodis, P., & Zhang, A. (2020) · Sex Roles

Hate Speech and Cyberbullying DetectionGender, Feminism, and Media

Cited by 91

Who uses web search for what: and how

Weber, I., & Jaimes, A. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining

Information Retrieval and Search BehaviorWeb Data Mining and Analysis

Cited by 91

Confident adaptive language modeling

Schuster, T., Fisch, A., Gupta, J., Dehghani, M., Bahri, D., Tran, V., Tay, Y., & Metzler, D. (2022) · Advances in Neural Information Processing Systems 35

Natural Language Processing TechniquesSpeech Recognition and Synthesis

Cited by 91

Learning Unsupervised Metaformer for Anomaly Detection

Wu, J.-C., Chen, D.-J., Fuh, C.-S., & Liu, T.-L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Anomaly Detection Techniques and ApplicationsIndustrial Vision Systems and Defect Detection

Cited by 91

From Word Embeddings to Pre-Trained Language Models: A State-of-the-Art Walkthrough

Mars, M. (2022) · Applied Sciences

Topic ModelingNatural Language Processing Techniques

Cited by 91

Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms

Roimi, M., Neuberger, A., Shrot, A., Paul, M., Geffen, Y., & Bar-Lavie, Y. (2020) · Intensive Care Medicine

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 91

Multimodal End-to-End Sparse Model for Emotion Recognition

Dai, W., Cahyawijaya, S., Liu, Z., & Fung, P. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 91

Applications of LLMs for Generating Cyber Security Exercise Scenarios

Mudassar Yamin, M., Hashmi, E., Ullah, M., & Katt, B. (2024) · IEEE Access

Information and Cyber SecurityAdversarial Robustness in Machine Learning

Cited by 91

Fairness Under Composition

Dwork, C., & Ilvento, C. (2019) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 91

Evaluating the ChatGPT family of models for biomedical reasoning and classification

Chen, S., Li, Y., Lu, S., Van, H., Aerts, H. J. W. L., Savova, G. K., & Bitterman, D. S. (2024) · Journal of the American Medical Informatics Association

Topic ModelingBiomedical Text Mining and Ontologies

Cited by 91

Comparing the Reliability of Amazon Mechanical Turk and Survey Monkey to Traditional Market Research Surveys

Bentley, F. R., Daskalova, N., & White, B. (2017) · Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems

Mobile Crowdsensing and CrowdsourcingSurvey Methodology and Nonresponse

Cited by 91

A controlled study of agoraphobia and the independent effect of virtual reality exposure therapy

Malbos, E., Rapee, R. M., & Kavakli, M. (2013) · Australian & New Zealand Journal of Psychiatry

Virtual Reality Applications and ImpactsAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Cited by 91

Using mobile GPU for general-purpose computing – a case study of face recognition on smartphones

Cheng, K.-T., & Wang, Y.-C. (2011) · Proceedings of 2011 International Symposium on VLSI Design, Automation and Test

Parallel Computing and Optimization TechniquesEmbedded Systems Design Techniques

Cited by 91

Generalization of Quantum Error Correction via the Heisenberg Picture

Bény, C., Kempf, A., & Kribs, D. W. (2007) · Physical Review Letters

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 91

Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences

Benou, A., Veksler, R., Friedman, A., & Riklin Raviv, T. (2017) · Medical Image Analysis

Image and Signal Denoising MethodsAdvanced MRI Techniques and Applications

Cited by 91

Multi-attention Network for One Shot Learning

Wang, P., Liu, L., Shen, C., Huang, Z., Van Den Hengel, A., & Shen, H. T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 91

NSML: Meet the MLaaS platform with a real-world case study

Kim, H., Kim, M., Seo, D., Kim, J., Park, H., Park, S., Jo, H., Kim, K., Yang, Y., Kim, Y., et al. (2018) · arXiv

Cloud Computing and Resource ManagementScientific Computing and Data Management

Cited by 91

Revolutionizing Visuals: The Role of Generative AI in Modern Image Generation

Bansal, G., Nawal, A., Chamola, V., & Herencsar, N. (2024) · ACM Transactions on Multimedia Computing, Communications, and Applications

Generative Adversarial Networks and Image SynthesisDigital Media and Visual Art

Cited by 91

Artificial intelligence in food science and nutrition: A narrative review

Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews

Artificial Intelligence ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 91

An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences

Guo, W., Tondi, B., & Barni, M. (2022) · IEEE Open Journal of Signal Processing

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 91

Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance

Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Advanced Image and Video Retrieval TechniquesInformation Retrieval and Search Behavior

Cited by 91